The BOED evaluates the optimality of a set of experiments by measuring information gain in the interest parameter due to the experimental outcome [1]}. In the context of hard attention, an experiment is to attend a location \(l\)  and observe a corresponding glimpse \(g=g(x,l)\) . An experiment of attending a location \(l\)  is optimal if it gains maximum information about the class label \(y\) . We can evaluate optimality of attending a specific location by measuring several metrics such as feature variance [2]}, uncertainty in the prediction [3]}, expected Shannon information [4]}. For a sequential model, information gain \( D_{\mathrm {KL}}[p(y|g, l, h_{t-1})||p(y|h_{t-1})]\)  is an ideal metric. It measures the change in the entropy of the class distribution from one time step to the next due to observation of a glimpse \(g\)  at location \(l\)  [5]}, [6]}.
